Recognising cursive Arabic text using a speech recognition system

Authors:
M. S. Khorsheed
Affiliations:
Computer & Electronics Research Institute, King AbdulAziz City, for Science and Technology, Riyadh, Saudi Arabia
Venue:
ICAI'06 Proceedings of the 7th WSEAS International Conference on Automation & Information
Year:
2006

Citing 3
Cited 0

Fundamentals of speech recognition

Fundamentals of speech recognition
Recognising handwritten Arabic manuscripts using a single hidden Markov model

Pattern Recognition Letters
Off-line recognition of handwritten Korean and alphanumeric characters using hidden Markov models

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a system to recognise cursive Arabic typewritten text. The system is built using the Hidden Markov Model Toolkit (HTK) which is a portable toolkit for speech recognition system. The proposed system decomposes the page into its text lines and then extracts a set of simple statistical features from small overlapped windows running through each text line. The feature vector sequence is injected to the global model for training and recognition purposes. A data corpus which includes Arabic text from two computer-generated fonts is used to assess the performance of the proposed system.